Computational Sustainability March 12, 2010 at 1:25PM – 2:40PM 1150 Snee Hall Optimizing Intervention Strategies in Food Animal Systems: modeling production, health and food safety .
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Computational Sustainability March 12, 2010 at 1:25PM – 2:40PM 1150 Snee Hall Optimizing Intervention Strategies in Food Animal Systems: modeling production, health and food safety
Elva Cha, DVM, PhD candidate, Rebecca Smith, DVM, PhD candidate, Zhao Lu, PhD, Research Associate and Cristina Lanzas, DVM, PhD, Research Associate and Yrjö T. Gröhn, DVM, MPVM, MS, PhD
Department of Population Medicine and Diagnostic Sciences,
College of Veterinary Medicine, Cornell University
Perhaps I should start with “ a disclaimer”…If I understood correctly, your approach is to assume the model is 'correct', then optimize the system.As veterinarians, our responsibility is to build a model based on subject matter representing reality.Of course, our goal is to find the optimal way to control disease (not necessarily eradication). And sometimes we need to learn the economically optimal way to coexist with them.
Food Supply Veterinary Medicine….all aspects of veterinary medicine's involvement in food supply systems, from traditional agricultural production to consumption.Modeling production, health and food safety: 1. Optimizing health and management decisions 2.Mathematical modeling of zoonotic infectious diseases (such as L. monozytogenes, E. coli, MDR salmonella and paratuberculosis).
Modeling production and health:
Project 1.“Optimal Clinical Mastitis Management in Dairy cows.” Elva Cha’s PhD research
Project 2. “Cost Effective Control Strategies for The Reduction of Johne’s Disease on Dairy Farms.” Zhao Lu, Research Associate and Becky Smith’s PhD research
2. Modeling Food Safety:
Project 3.“Food Animal Systems-Based Mathematical Models of Antibiotic Resistance among Commensal Bacteria.” Cristina Lanzas, Research Associate
Let’s start with…1. Modeling production and health: Our overall goal is to develop a comprehensive economic model, dynamic model (DP), to assist farmers in making treatment and culling decisions. Our 1st example:Elva Cha’s PhD research: “Optimal Clinical Mastitis Management in Dairy cows”
Mastitis (inflammation in mammary gland)Common, costly disease (major losses: milk yield, conception rates, and culling). The question we address is whether it is better to treat animals at the time CM is first observed or whether it is worthwhile collecting more information related to the nature of the pathogen involved (Gram-positive or Gram-negative, or even the actual pathogen), and then make a treatment decision.More information would seem to be of benefit when deciding what to do with diseased cows, but unless the additional tests provide a different recommended outcome, they are only an extra cost.
To do this
What is this model?
At each stage, the state of the system is observed
And a decision is made
This decision influences stochastically the state to be observed at the next stage
Depending on the state and decision, a reward is gained
Why is this a limitation? Can’t we simply expand the model?
Structure of the hierarchic Markov process optimization model:First level:5 milk yield levelsSecond level:8 possible lactations and 2 carry-over mastitis states from previous lactation (yes/no).Third level:20 lactation stages (max calving interval of 20 months). 5 temporary milk yield levels (relative to permanent milk yield),9 pregnancy status levels (0=open, 1-7 months pregnant, and 8=to be dried off), one involuntary culled state13 mastitis states: 0=no mastitis 1 = 1st occurrence of CM (observed at the end of the stage), 2, 3, 4 = 1, 2, 3 and more months after 1st CM, 5 = 2nd CM, 6, 7, 8 = 1, 2, 3 and more months after 2nd CM, 9 = 3rd CM, 10, 11, 12 = 1, 2, 3 and more months after 3rd CM, CM events > 3 assigned same penalties as if they were 3rd occurrence.After deleting impossible stage-state combinations, the model described 560,725 stage-state combinations.
No longer only generic guidelines for the generic cow.
The DP recommendations could be tailored to the individual cow in real time according to her cow characteristics and economics of the herd.
Project 2: “Cost Effective Control Strategies for The Reduction of Johne’s Disease on Dairy Farms”Zhao Lu, PhD, Research Associate, and Becky Smith, DVM, PhD student
No clinical signs
However, it is difficult to control JD spread:
- Reduction of
dModeling of MAP transmission on a dairy herd
Vaccines are often imperfect Johne’s disease control
They may not prevent all infections
They may have effects other than decreasing susceptibility
Efficacy can be considered as the proportional effect on a rate or probability parameter in a compartmental model
5 vaccine effects:
Duration of latency
Duration of low-infectious period
Progression of clinical symptomsModeling the efficacy of an imperfect vaccine with multiple effects
(1-p)( Johne’s disease control-)
dModeling vaccine efficacy against JD
Known information: Johne’s disease control
annual test dates and results
Date of infection
Onset of low-shedding
Onset of high-shedding
True infection status (if all tests results were negative)Estimating vaccine efficacy against JD with field data
To estimate vaccine efficacy,
missing information must also be estimated
MCMC models are Bayesian statistical models, useful for disease modeling because they
Project 3: models“Develop, evaluate and improve food animal systems-based mathematical models of antimicrobial resistance among commensalbacteria”
Cristina Lanzas, DVM, PhD, Research Associate
Bergstrom and Feldgarden, 2008
Molecular mechanisms involved in the spread of antimicrobial resistance. Inter-cellular movement (horizontal spread) is the main cause of acquisition of resistance genes.
Salyers et al., 2004
- bacteria in the human and animal gastrointestinal tracts (small intestine supports ~ 10
The host population is divided according its epidemiological status (e.g. susceptible, infectious)
“Binary response”: Animal carries the bacteria carrying the resistance or not
Population dynamics of antibiotic-sensitive and –resistant bacteria
Linked to antibiotic exposure
Emergence of resistance during antibiotic treatment
Transmission of resistant clones
Individuals colonized with either susceptible or resistant strains
Fitness cost linked to microbial growth
Within host dynamics of antimicrobial resistance bacteria in the human and animal gastrointestinal tracts (small intestine supports ~ 10dissemination
Microbial growth for sensitive and resistant strains with horizontal gene transfer
Plasmid loss during segregation
Percentage of resistant bacteria 24 h after the end of the antimicrobial treatment
γ bacteria in the human and animal gastrointestinal tracts (small intestine supports ~ 10
Between host dynamics of antimicrobial resistance dissemination
Ayscue et al., FoodbornePathogDis, 6:461-470 (2009)
This metapopulation approach is suitable for modeling the dynamics of antimicrobial resistance dissemination. Pharmacokinetics and pharmacodynamics and biological fitness of antimicrobial resistance can be integrated.
Growing ruminant heifer
Bred ruminant heifer
Assuming three types of ecological patches (water, environment and n animals) and assuming indirect transmission (bacteria are transmitted to animals through water and environment):
For the j animal:
Potential students projects environment and